Predicting the responses of a cell under perturbations may bring important benefits to drug discovery and personalized therapeutics. In this work, we propose a novel graph variational Bayesian causal inference framework to predict a cell's gene expressions under counterfactual perturbations (perturbations that this cell did not factually receive), leveraging information representing biological knowledge in the form of gene regulatory networks (GRNs) to aid individualized cellular response predictions. Aiming at a data-adaptive GRN, we also developed an adjacency matrix updating technique for graph convolutional networks and used it to refine GRNs during pre-training, which generated more insights on gene relations and enhanced model performance. Additionally, we propose a robust estimator within our framework for the asymptotically efficient estimation of marginal perturbation effect, which is yet to be carried out in previous works. With extensive experiments, we exhibited the advantage of our approach over state-of-the-art deep learning models for individual response prediction.
翻译:摘要:预测细胞在干扰下的响应可能为药物发现和个性化治疗带来重要的好处。在这项工作中,我们提出了一种新的图变分贝叶斯因果推断框架,以预测在反事实干扰下(尚未实际接收的干扰)下的细胞基因表达,并利用基因调控网络 ( GRN ) 代表生物知识的信息来帮助个体化的细胞响应预测。为了实现数据自适应 GRN,我们还开发了一种邻接矩阵更新技术,用于图卷积网络的预训练,并用它来优化 GRN,从而生成更多有关基因关系的信息并提高模型性能。此外,我们提出了一个稳健的估计器,在我们的框架内为渐进有效的边际干扰效应估计提供了解决方案,这在以前的工作中尚未实现。通过大量实验,在个体响应预测方面,我们展示了我们方法优于现有深度学习模型的优越性。